First-order causal process for causal modelling with instantaneous and cross-temporal relations

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Conference Proceeding
Proceedings of the International Joint Conference on Neural Networks, 2017, 2017-May pp. 380 - 387
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© 2017 IEEE. Motivated by the real damped simple harmonic oscillator (SHO) system, in this paper, we propose a process interpretation of causality and the first-order causal process (FoCP) model for temporal causal modelling. Compared with existing causal models that are able to model feedbacks, such as the structural equation model (SEM) and the structure vector autoregressive (SVAR) model, the FoCP model entails a novel 2-stage evolution semantic for instantaneous and cross-temporal causal relations existing in many real world dynamic systems. Graphical representations are developed to illustrate the causal structure compactly. Useful properties of the new model are identified and used to develop a conditional independence based algorithm for learning the causal structure from a multivariate time series dataset. Experiments on both simulated and real data validate the feasibility of the method to discover simple while meaningful causal structures of dynamic systems.
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